Original Research Predicting Recreational Runners’ Marathon Performance Time During Their Training Preparation Jonathan Esteve-Lanao, 1 Sebasti ´ an Del Rosso, 3 Eneko Larumbe-Zabala, 4 Claudia Cardona, 1,5 Alberto Alcocer-Gamboa, 2 and Daniel A. Boullosa 6,7 1 All In Your Mind Training System TM, Yucat ´ an, Mexico; 2 Winhealth Medical Center, Merida, Yucat ´ an, Mexico; 3 Post-Graduate Program in Physical Education, Catholic University of Brasilia, Brasilia, Brazil; 4 Clinical Research Institute, Texas Tech University Health Sciences Center, Lubbock, Texas; 5 Health Sciences Faculty, University of the Valley of Mexico, M ´ erida, Yucat ´ an, Mexico; 6 iLOAD Solutions, Brasilia, DF, Brazil; and 7 Sport and Exercise Science, James Cook University, Townsville, Australia Abstract Esteve-Lanao, J, Del Rosso, S, Larumbe-Zabala, E, Cardona, C, Alcocer-Gamboa, A, and Boullosa, DA. Predicting marathon performance time throughout the training preparation in recreational runners. J Strength Cond Res XX(X): 000–000, 2019—The objective of this study was to predict marathon performance at different time points along the season using different speeds derived from ventilatory thresholds and running economy (RE). Sixteen recreational runners (8 women and 8 men) completed a 16-week marathon training macrocycle. Aerobic threshold (AeT), anaerobic threshold (AnT), and maximal oxygen uptake were assessed at the beginning of the season, whereas speeds eliciting training zones at AeT and AnT, and RE were evaluated at 5-time points during the season (M1–M5). Analyses of variance and hierarchical regression analyses were conducted. Training improved AeT and AnT speeds at M2 vs. M1 (p 5 0.001) and remained significantly higher at M3, M4, and M5 (p 5 0.001). There was a significant effect of time (p 5 0.003) for RE, being higher at M4 and M5 compared with M1 and M3. Significant correlations were found between marathon performance and speeds at AeT and AnT at every time point (r 5 0.81–0.94; p , 0.05). Speed at AnT represented the main influence (65.9 and 71.41%) in the final time prediction at M1 and M2, whereas speed at AeT took its place toward the end of the macrocycle (76.0, 80.4, and 85.0% for M3, M4, and M5, respectively). In conclusion, assessment of speeds at AeT and AnT permits for reasonable performance prediction during the training preparation, therefore avoiding maximal testing while monitoring 2 fundamental training speeds. Future research should verify if these findings are applicable to runners of different levels and other periodization models. Key Words: running performance prediction, endurance training, anaerobic threshold, running economy Introduction Marathons have become the most popular endurance event with millions of recreational athletes participating each year in com- petitions worldwide. Marathon represents the ultimate challenge for many runners, and therefore, they must prepare themselves both physically and mentally for such a strenuous event (3,33). In this scenario, improving an athlete’s physical and physiological capacities is one of the biggest puzzles to be addressed by athletes, coaches, and sport physiologists. This, in time, implies the knowledge of those factors associated with successful perfor- mance in the desired athletic event. As an example, there is enough evidence to support the notion that maximal oxygen uptake (V . O 2 max) is one of the most important physiological factors contributing to endurance running performance (2,16,22,24,26). However, V . O 2 max does not solely explain en- durance running performance, particularly in long-distance events such the marathon. For instance, although high values of V . O 2 max could be important for performance time, long-distance runners usually have lower values when compared with shorter distance athletes because other factors such as fractional utiliza- tion of (%) V . O 2 max or the highest sustainable %V . O 2 max and running economy (RE) become more important for performance time in long-distance running (16,17,26,29). To improve running performance, it is essential to establish which of those factors contributing to competitive performance are to be enhanced by training. To accomplish this goal, it is required that performance could be predicted because, from a practical point of view, predicting competitive time will re- trieve useful information for coaches and athletes allowing them to better prepare for both training (e.g., tempo runs) and com- petition (e.g., pacing strategies). Moreover, predicting running performance helps to understand the factors associated with the ability to sustain a given speed for a given exercise duration leading to a successful performance time. In this sense, several approaches have been used to predict running performance time. For instance, expected times based on shorter distances are commonly used to estimate an athlete’s potential during longer events (21). Particularly, nomograms are an appealing method for predicting performance time (5). However, performance time prediction using nomograms is based on the assumption of equivalence between performances in different events. There- fore, although its simplicity and validity can be attractive for coaches and athletes, given that it requires the performance times of 2 distances and marathons and shorter races are usually run few times on a year, this assumption is not always met. In addition, the accuracy in the predictions seems to be higher when performance is obtained by interpolation instead of ex- trapolation (5). Address correspondence to Jonathan Esteve-Lanao, jonathan.esteve@ allinyourmind.es. Journal of Strength and Conditioning Research 00(00)/1–7 ª 2019 National Strength and Conditioning Association 1 Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.